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水力发电学报 ›› 2026, Vol. 45 ›› Issue (10): 90-99.doi: 10.11660/slfdxb.20261009

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混蓄电站耦合调水系统多目标优化调度研究

  

  • 出版日期:2026-10-25 发布日期:2026-10-25

Multi-objective optimal scheduling of hybrid pumped storage power stations coupled with water transfer systems

  • Online:2026-10-25 Published:2026-10-25

摘要: 针对混合式抽水蓄能与调水耦合系统日前调度中发电、供水与调水协调困难的问题,构建多目标协同优化调度模型,采用多目标麻雀搜索算法生成非劣解集。以湖南镇-黄坛口-乌溪江混蓄系统为例,将优化模型结果与负荷分配模拟算法的基准方案进行对比,结果表明:不同目标之间存在权衡关系,发电与供水、调水呈竞争关系,供水与调水呈相容关系;相对模拟算法的基准方案,多目标优化模型的非劣解集在发电、供水和调水3个目标上实现提升,以枯季典型日为例,发电量提升5.52万kW·h,供水保证程度提高7.50%,外调水量增加1.17万m3。本研究建立的混蓄-调水耦合多目标优化调度模型可为水网、电网系统联合调度与协同运行提供一定参考。

关键词: 水库调度, 梯级水库群, 混合式抽水蓄能电站, 多目标优化, 跨区域调水, 日前调度

Abstract: A multi-objective coordinated optimal scheduling model is developed for the day-ahead operation of a hybrid pumped storage-water transfer coupled system that is faced with scheduling difficulty involving how to coordinate power generation, water supply, and water transfer. It uses a multi-objective sparrow search algorithm to generate a Pareto-optimal solution set. We compare its optimization results with those of a rule-based load allocation benchmark in a case study of the Hunanzhen-Huangtankou-Wuxijiang hybrid pumped storage system. The results indicate that scheduling such a coupled system must consider significant trade-offs among the objectives-a competitive relationship of power generation versus water supply and water transfer, and an overall compatibility of water supply and water transfer. The Pareto solutions generated using this new model achieve simultaneous improvement on all the three objectives-power generation, water supply reliability, and transferred water volume-in comparison with the benchmark scheme. For a typical dry-season day, power generation increases by 55.2 MW·h, water supply reliability improves by 7.50%, and transferred water volume increases by 11,700 m3. Our scheduling model proves effective and useful for the coordinated operation of water and power networks.

Key words: reservoir operation, cascade reservoirs, hybrid pumped storage power station, multi-objective optimization, inter-basin water transfer, day-ahead scheduling

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